System Dynamics (SD) may be viewed as a process of designing ROBUST systems. The concept of ROBUSTNESS leads to a need for analyzing the effects on SD models of both parameter changes and stochastic inputs. It is demonstrated that the effects of large parameter changes can be measured by the use of hill climbing techniques given efficient computation. The paper describes the traditional ways of assessing sensitivities in SD models, together with methods based on perturbation techniques which unify the parameter and stochastic sensitivity problems. The computational characteristics of the various methods are analysed and the factors that affect their computational efficiency are discussed.The paper discusses the results of experiments to determine the accuracy and speed of the various methods on a 7 state variable, 16 parameter model and on a 70 state variable, 160 parameter model derived from it. The perturbation methods yield acceptable accuracy and for the models described reduce computer time by a factor of between 9 and 25. Compiler changes discussed in the paper would make sensitivity analysis easier and quicker and would improve techniques elsewhere in System Dynamic.

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Date created

• 1976

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• Document

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• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

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• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

  
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System Dynamic Society Records

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• This collection is part of the University Archives